Overview

Dataset statistics

Number of variables24
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory275.8 KiB
Average record size in memory192.1 B

Variable types

Categorical14
Numeric10

Alerts

annee_experience_totale is highly overall correlated with annees_dans_l_entreprise and 2 other fieldsHigh correlation
annees_dans_l_entreprise is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
annees_dans_le_poste_actuel is highly overall correlated with annees_dans_l_entreprise and 2 other fieldsHigh correlation
annes_sous_responsable_actuel is highly overall correlated with annees_dans_l_entreprise and 2 other fieldsHigh correlation
duree_moyenne_par_poste is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
nombre_participation_pee is highly overall correlated with statut_maritalHigh correlation
revenu_mensuel is highly overall correlated with annee_experience_totaleHigh correlation
statut_marital is highly overall correlated with nombre_participation_peeHigh correlation
annees_dans_l_entreprise has 44 (3.0%) zerosZeros
annees_dans_le_poste_actuel has 244 (16.6%) zerosZeros
nb_formations_suivies has 54 (3.7%) zerosZeros
annes_sous_responsable_actuel has 263 (17.9%) zerosZeros
Ratio_Stagnation has 581 (39.5%) zerosZeros

Reproduction

Analysis started2026-01-03 14:11:30.385373
Analysis finished2026-01-03 14:11:34.361993
Duration3.98 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2026-01-03T15:11:34.380308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.400415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2026-01-03T15:11:34.426489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.446044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2026-01-03T15:11:34.470673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.491921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2026-01-03T15:11:34.517615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.537478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2026-01-03T15:11:34.562949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.582002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1054 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Length

2026-01-03T15:11:34.606450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.623792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%
Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:34.640977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2026-01-03T15:11:34.661917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

genre
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
882 
1
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Length

2026-01-03T15:11:34.688348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:34.705422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring characters

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

revenu_mensuel
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:34.726766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2026-01-03T15:11:34.760037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
23803
 
0.2%
55623
 
0.2%
63473
 
0.2%
34523
 
0.2%
25593
 
0.2%
24513
 
0.2%
27413
 
0.2%
61423
 
0.2%
24043
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

statut_marital
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Marié(e)
673 
Célibataire
470 
Divorcé(e)
327 

Length

Max length11
Median length10
Mean length9.4040816
Min length8

Characters and Unicode

Total characters13824
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCélibataire
2nd rowMarié(e)
3rd rowCélibataire
4th rowMarié(e)
5th rowMarié(e)

Common Values

ValueCountFrequency (%)
Marié(e)673
45.8%
Célibataire470
32.0%
Divorcé(e)327
22.2%

Length

2026-01-03T15:11:35.050385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.067614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
marié(e673
45.8%
célibataire470
32.0%
divorcé(e327
22.2%

Most occurring characters

ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1940
14.0%
a1613
11.7%
r1470
10.6%
é1470
10.6%
e1470
10.6%
(1000
7.2%
)1000
7.2%
M673
 
4.9%
C470
 
3.4%
l470
 
3.4%
Other values (6)2248
16.3%

poste
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Cadre Commercial
326 
Assistant de Direction
292 
Consultant
259 
Tech Lead
145 
Manager
131 
Other values (4)
317 

Length

Max length23
Median length19
Mean length15.168027
Min length7

Characters and Unicode

Total characters22297
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCadre Commercial
2nd rowAssistant de Direction
3rd rowConsultant
4th rowAssistant de Direction
5th rowConsultant

Common Values

ValueCountFrequency (%)
Cadre Commercial326
22.2%
Assistant de Direction292
19.9%
Consultant259
17.6%
Tech Lead145
9.9%
Manager131
8.9%
Senior Manager102
 
6.9%
Représentant Commercial83
 
5.6%
Directeur Technique80
 
5.4%
Ressources Humaines52
 
3.5%

Length

2026-01-03T15:11:35.091365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.114396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
commercial409
14.4%
cadre326
11.5%
assistant292
10.3%
de292
10.3%
direction292
10.3%
consultant259
9.1%
manager233
8.2%
tech145
 
5.1%
lead145
 
5.1%
senior102
 
3.6%
Other values (5)347
12.2%

Most occurring characters

ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)22297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2586
11.6%
a2032
 
9.1%
n1735
 
7.8%
r1657
 
7.4%
t1640
 
7.4%
i1599
 
7.2%
s1426
 
6.4%
1372
 
6.2%
o1114
 
5.0%
c1058
 
4.7%
Other values (18)6078
27.3%

annee_experience_totale
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.148026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2026-01-03T15:11:35.178559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

annees_dans_l_entreprise
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.206068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2026-01-03T15:11:35.234313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

annees_dans_le_poste_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.258682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2026-01-03T15:11:35.284054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Length

2026-01-03T15:11:35.315406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.334558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

nombre_participation_pee
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2026-01-03T15:11:35.359717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.381189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

nb_formations_suivies
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.398821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2026-01-03T15:11:35.418711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

distance_domicile_travail
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.443573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2026-01-03T15:11:35.468465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

niveau_education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2026-01-03T15:11:35.496387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.517292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Occasionnel
1043 
Frequent
277 
Aucun
150 

Length

Max length11
Median length11
Mean length9.822449
Min length5

Characters and Unicode

Total characters14439
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOccasionnel
2nd rowFrequent
3rd rowOccasionnel
4th rowFrequent
5th rowOccasionnel

Common Values

ValueCountFrequency (%)
Occasionnel1043
71.0%
Frequent277
 
18.8%
Aucun150
 
10.2%

Length

2026-01-03T15:11:35.542506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.559602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
occasionnel1043
71.0%
frequent277
 
18.8%
aucun150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n2513
17.4%
c2236
15.5%
e1597
11.1%
a1043
7.2%
s1043
7.2%
i1043
7.2%
O1043
7.2%
o1043
7.2%
l1043
7.2%
u577
 
4.0%
Other values (5)1258
8.7%

annes_sous_responsable_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.580635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2026-01-03T15:11:35.605025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

Ratio_Stagnation
Real number (ℝ)

Zeros 

Distinct113
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29023166
Minimum0
Maximum1
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.634890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.16666667
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.34052096
Coefficient of variation (CV)1.173273
Kurtosis-0.45861468
Mean0.29023166
Median Absolute Deviation (MAD)0.16666667
Skewness0.96010985
Sum426.64054
Variance0.11595453
MonotonicityNot monotonic
2026-01-03T15:11:35.666858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0581
39.5%
1135
 
9.2%
0.283
 
5.6%
0.333333333376
 
5.2%
0.2556
 
3.8%
0.166666666739
 
2.7%
0.536
 
2.4%
0.142857142928
 
1.9%
0.87527
 
1.8%
0.124
 
1.6%
Other values (103)385
26.2%
ValueCountFrequency (%)
0581
39.5%
0.027027027031
 
0.1%
0.029411764711
 
0.1%
0.03030303031
 
0.1%
0.038461538461
 
0.1%
0.041666666672
 
0.1%
0.045454545451
 
0.1%
0.047619047621
 
0.1%
0.052
 
0.1%
0.052631578951
 
0.1%
ValueCountFrequency (%)
1135
9.2%
0.92307692311
 
0.1%
0.91666666672
 
0.1%
0.90909090911
 
0.1%
0.911
 
0.7%
0.88888888895
 
0.3%
0.88235294122
 
0.1%
0.87527
 
1.8%
0.85714285713
 
0.2%
0.84615384621
 
0.1%

match_etudes_poste
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1105 
0
365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

Length

2026-01-03T15:11:35.697680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-03T15:11:35.717073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

Most occurring characters

ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11105
75.2%
0365
 
24.8%

duree_moyenne_par_poste
Real number (ℝ)

High correlation 

Distinct178
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1934778
Minimum0
Maximum38
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2026-01-03T15:11:35.738802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.6
median3
Q35
95-th percentile10.5
Maximum38
Range38
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation4.0355036
Coefficient of variation (CV)0.9623286
Kurtosis14.201804
Mean4.1934778
Median Absolute Deviation (MAD)1.8333333
Skewness2.9597957
Sum6164.4123
Variance16.285289
MonotonicityNot monotonic
2026-01-03T15:11:35.769501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5123
 
8.4%
385
 
5.8%
0.583
 
5.6%
170
 
4.8%
268
 
4.6%
2.565
 
4.4%
465
 
4.4%
650
 
3.4%
1039
 
2.7%
1.536
 
2.4%
Other values (168)786
53.5%
ValueCountFrequency (%)
011
 
0.7%
0.32
 
0.1%
0.33333333331
 
0.1%
0.3751
 
0.1%
0.43
 
0.2%
0.42857142861
 
0.1%
0.44444444443
 
0.2%
0.583
5.6%
0.55555555561
 
0.1%
0.57142857141
 
0.1%
ValueCountFrequency (%)
381
 
0.1%
371
 
0.1%
342
 
0.1%
281
 
0.1%
251
 
0.1%
233
0.2%
222
 
0.1%
217
0.5%
203
0.2%
192
 
0.1%

Interactions

2026-01-03T15:11:33.970356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:30.910801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.196878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.481310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.763634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:32.040753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:32.318850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:33.149312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-03T15:11:33.647866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-03T15:11:34.224854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.169471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.450620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:31.733981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:32.013276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:32.291377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:33.122682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:33.396635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:33.674240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-03T15:11:33.943779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-03T15:11:35.800676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Ratio_Stagnationa_quitte_l_entrepriseannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuelannes_sous_responsable_actuelaugementation_salaire_precedentedistance_domicile_travailduree_moyenne_par_postefrequence_deplacementgenreheure_supplementairesmatch_etudes_postenb_formations_suiviesniveau_educationnombre_participation_peenote_evaluation_precedenteposterevenu_mensuelsatisfaction_employee_environnementsatisfaction_employee_equilibre_pro_persosatisfaction_employee_equipesatisfaction_employee_nature_travailstatut_marital
Ratio_Stagnation1.0000.0970.1220.2170.2650.232-0.043-0.0060.1460.0100.0260.0000.0670.0060.0470.0000.0330.0620.1060.0000.0360.0300.0150.000
a_quitte_l_entreprise0.0971.0000.2080.1730.1690.1790.0000.0670.1390.1230.0090.2430.0260.0790.0000.1980.1320.2310.2170.1150.0950.0390.0990.173
annee_experience_totale0.1220.2081.0000.5940.4930.495-0.026-0.0030.6210.0000.0000.0000.000-0.0140.0950.0640.0000.2930.7100.0000.0000.0310.0240.069
annees_dans_l_entreprise0.2170.1730.5941.0000.8540.843-0.0540.0110.6660.0000.0660.0180.0000.0010.0710.0120.0530.1880.4640.0310.0200.0000.0000.000
annees_dans_le_poste_actuel0.2650.1690.4930.8541.0000.725-0.0260.0140.5540.0000.0790.0420.0000.0050.0290.0230.0000.1320.3950.0360.0250.0000.0000.040
annes_sous_responsable_actuel0.2320.1790.4950.8430.7251.000-0.0260.0040.5630.0640.0000.0000.000-0.0120.0000.0300.0440.1180.3650.0000.0310.0000.0000.000
augementation_salaire_precedente-0.0430.000-0.026-0.054-0.026-0.0261.0000.030-0.0190.0300.0490.0000.000-0.0040.0210.0000.0360.000-0.0340.0000.0000.0270.0000.000
distance_domicile_travail-0.0060.067-0.0030.0110.0140.0040.0301.0000.0120.0230.0300.0660.049-0.0250.0000.0150.0280.0000.0030.0000.0000.0250.0000.000
duree_moyenne_par_poste0.1460.1390.6210.6660.5540.563-0.0190.0121.0000.0000.0700.0000.0250.0170.0410.0080.0270.1330.4930.0150.0000.0430.0000.043
frequence_deplacement0.0100.1230.0000.0000.0000.0640.0300.0230.0001.0000.0370.0240.0000.0000.0000.0000.0160.0000.0250.0000.0000.0000.0000.035
genre0.0260.0090.0000.0660.0790.0000.0490.0300.0700.0371.0000.0310.0000.0000.0000.0000.0000.0740.0460.0000.0000.0000.0000.032
heure_supplementaires0.0000.2430.0000.0180.0420.0000.0000.0660.0000.0240.0311.0000.0360.0990.0010.0000.0000.0000.0000.0600.0000.0250.0220.000
match_etudes_poste0.0670.0260.0000.0000.0000.0000.0000.0490.0250.0000.0000.0361.0000.0000.0000.0000.0000.4520.0970.0000.0130.0000.0000.026
nb_formations_suivies0.0060.079-0.0140.0010.005-0.012-0.004-0.0250.0170.0000.0000.0990.0001.0000.0270.0000.0130.000-0.0350.0000.0000.0000.0210.000
niveau_education0.0470.0000.0950.0710.0290.0000.0210.0000.0410.0000.0000.0010.0000.0271.0000.0270.0000.0510.0940.0190.0000.0160.0150.000
nombre_participation_pee0.0000.1980.0640.0120.0230.0300.0000.0150.0080.0000.0000.0000.0000.0000.0271.0000.0220.0390.0560.0000.0190.0300.0000.581
note_evaluation_precedente0.0330.1320.0000.0530.0000.0440.0360.0280.0270.0160.0000.0000.0000.0130.0000.0221.0000.0000.0460.0340.0000.0000.0000.024
poste0.0620.2310.2930.1880.1320.1180.0000.0000.1330.0000.0740.0000.4520.0000.0510.0390.0001.0000.4230.0000.0290.0300.0000.061
revenu_mensuel0.1060.2170.7100.4640.3950.365-0.0340.0030.4930.0250.0460.0000.097-0.0350.0940.0560.0460.4231.0000.0000.0000.0430.0000.061
satisfaction_employee_environnement0.0000.1150.0000.0310.0360.0000.0000.0000.0150.0000.0000.0600.0000.0000.0190.0000.0340.0000.0001.0000.0000.0000.0000.019
satisfaction_employee_equilibre_pro_perso0.0360.0950.0000.0200.0250.0310.0000.0000.0000.0000.0000.0000.0130.0000.0000.0190.0000.0290.0000.0001.0000.0000.0000.000
satisfaction_employee_equipe0.0300.0390.0310.0000.0000.0000.0270.0250.0430.0000.0000.0250.0000.0000.0160.0300.0000.0300.0430.0000.0001.0000.0000.025
satisfaction_employee_nature_travail0.0150.0990.0240.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0210.0150.0000.0000.0000.0000.0000.0000.0001.0000.000
statut_marital0.0000.1730.0690.0000.0400.0000.0000.0000.0430.0350.0320.0000.0260.0000.0000.5810.0240.0610.0610.0190.0000.0250.0001.000

Missing values

2026-01-03T15:11:34.275111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-03T15:11:34.329907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

satisfaction_employee_environnementnote_evaluation_precedentesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_persoheure_supplementairesaugementation_salaire_precedentegenrerevenu_mensuelstatut_maritalposteannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuela_quitte_l_entreprisenombre_participation_peenb_formations_suiviesdistance_domicile_travailniveau_educationfrequence_deplacementannes_sous_responsable_actuelRatio_Stagnationmatch_etudes_posteduree_moyenne_par_poste
02341111115993CélibataireCadre Commercial86410012Occasionnel50.00000000.888889
13224302305130Marié(e)Assistant de Direction1010701381Frequent70.10000015.000000
24232311502090CélibataireConsultant70010322Occasionnel00.00000011.000000
34333311112909Marié(e)Assistant de Direction88700334Frequent00.37500014.000000
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